Code
# load_packages()# load_packages()# Load soil data from sampling locations
bern_data <- readr::read_csv(
here::here("data-raw/soildata/berne_soil_sampling_locations.csv")
)
# Display data
head(bern_data) |> knitr::kable()| site_id_unique | timeset | x | y | dataset | dclass | waterlog.30 | waterlog.50 | waterlog.100 | ph.0.10 | ph.10.30 | ph.30.50 | ph.50.100 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4_26-In-005 | d1968_1974_ptf | 2571994 | 1203001 | validation | poor | 0 | 0 | 1 | 6.071733 | 6.227780 | 7.109235 | 7.214589 |
| 4_26-In-006 | d1974_1978 | 2572149 | 1202965 | calibration | poor | 0 | 1 | 1 | 6.900000 | 6.947128 | 7.203502 | 7.700000 |
| 4_26-In-012 | d1974_1978 | 2572937 | 1203693 | calibration | moderate | 0 | 1 | 1 | 6.200000 | 6.147128 | 5.603502 | 5.904355 |
| 4_26-In-014 | d1974_1978 | 2573374 | 1203710 | validation | well | 0 | 0 | 0 | 6.600000 | 6.754607 | 7.200000 | 7.151129 |
| 4_26-In-015 | d1968_1974_ptf | 2573553 | 1203935 | validation | moderate | 0 | 0 | 1 | 6.272715 | 6.272715 | 6.718392 | 7.269008 |
| 4_26-In-016 | d1968_1974_ptf | 2573310 | 1204328 | calibration | poor | 0 | 0 | 1 | 6.272715 | 6.160700 | 5.559031 | 5.161655 |
The dataset on soil samples from Bern holds 13 variables for 1052 entries (more information here):
site_id_unique: The location’s unique site id.
timeset: The sampling year and information on sampling type for soil pH (no label: CaCl\(_2\) laboratory measurement, field: indicator solution used in field, ptf: H\(_2\)O laboratory measurement transferred by pedotransfer function).
x: The x (easting) coordinates in meters following the (CH1903/LV03) system.
y: The y (northing) coordinates in meters following the (CH1903/LV03) system.
dataset: Specification whether a sample is used for model calibration or validation (this is based on randomization to ensure even spatial coverage).
dclass: Soil drainage class
waterlog.30, waterlog.50, waterlog.100: Specification whether soil was water logged at 30, 50, or 100 cm depth (0 = No, 1 = Yes).
ph.0.10, ph.10.30, ph.30.50, ph.50.100: Average soil pH between 0-10, 10-30, 30-50, and 50-100 cm depth.
Now, let’s load all the covariates that we want to produce our soil maps.
# Get a list with the path to all raster files
list_raster <-
base::list.files(
here::here("data-raw/geodata/covariates/"),
full.names = T
)
# Display data (lapply to clean names)
lapply(
list_raster,
function(x) sub(".*/(.*)", "\\1", x)
) |>
unlist() |>
head(5) |>
print()[1] "be_gwn25_hdist.tif" "be_gwn25_vdist.tif" "cindx10_25.tif"
[4] "cindx50_25.tif" "geo500h1id.tif"
The output above shows the first five raster files with rather cryptic names. For this tutorial, we do not need to know all of the 91 rasters files that we are loading here but in a scientific context, you should know your data better of course. Let’s look at one of these raster files to get a better feeling for our data:
# Load a raster file as example: Picking the slope profile at 2m resolution
raster_example <- terra::rast(list_raster[74])
raster_exampleclass : SpatRaster
dimensions : 986, 2428, 1 (nrow, ncol, nlyr)
resolution : 20, 20 (x, y)
extent : 2568140, 2616700, 1200740, 1220460 (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95
source : Se_slope2m.tif
name : Se_slope2m
min value : 0.00000
max value : 85.11286
As shown in the output, a raster object has the following properties (among others, see ?terra::rast):
class: The class of the file, here a SpatRaster.
dimensions: The number of rows, columns, years (if temporal encoding).
resolution: The resolution of the coordinate system, here it is 20 in both axes.
extent: The extent of the coordinate system defined by min and max values on the x and y axes.
coord. ref.: Reference coordinate system. Here, the raster is encoded using the LV95 geodetic reference system from which the projected coordinate system CH1903+ is derived.
source: The name of the source file.
names: The name of the raster file (mostly the file name without file-specific ending)
min value: The lowest value of all cells.
max value: The highest value of all cells.
Now, let’s look at a visualisation of this raster file. Since we selected the slope at 2m resolution, we expect a relief-like map with a color gradient that indicates the steepness of the terrain.
# Plot raster example
terra::plot(raster_example)
# To have some more flexibility, we can plot this in the ggplot-style as such:
ggplot2::ggplot() +
tidyterra::geom_spatraster(data = raster_example) +
ggplot2::scale_fill_viridis_c(
na.value = NA,
option = "magma",
name = "Slope (%) \n"
) +
ggplot2::theme_bw() +
ggplot2::scale_x_continuous(expand = c(0, 0)) + # avoid gap between plotting area and axis
ggplot2::scale_y_continuous(expand = c(0, 0)) +
ggplot2::labs(title = "Slope of the Study Area")
Note that the second plot has different coordinates than the upper one. That is because the data was automatically projected to the World Geodetic System (WGS84, ESPG: 4326).
This looks already interesting but we can put our data into a bit more context. For example, a larger map background would be useful to get a better orientation of our location. Also, it would be nice to see where our sampling locations are and to differentiate these locations by whether they are part of the calibration or validation dataset. Bringing this all together requires some more understanding of plotting maps in R. So, don’t worry if you do not understand everything in the code chunk below and enjoy the visualisations:
# To get our map working correctly, we have to ensure that all the input data
# is in the same coordinate system. Since our Berne data is in the Swiss
# coordinate system, we have to transform the sampling locations to the
# World Geodetic System first.
# For the raster:
r <- terra::project(raster_example, "+init=EPSG:4326")
# For the sampling locations:
# Function from Stackoverflow:
# https://stackoverflow.com/questions/49536664/r-transforming-coordinates-inside-the-data-frame
change_coords <-function(data, from_CRS, to_CRS) {
# Load required package
require(sp)
# Turn string into CRS
from_CRS = CRS(from_CRS)
to_CRS = CRS(to_CRS)
new <-
as.data.frame(
spTransform(
SpatialPointsDataFrame(
coords = data.frame(
lon = data$lon,
lat = data$lat),
data = data.frame(
id = data$id,
lon_old = data$lon,
lat_old = data$lat),
proj4string = from_CRS),
to_CRS
)
)
new <-
new |>
dplyr::select(coords.x1, coords.x2, id) |>
dplyr::rename(lon = coords.x1,
lat = coords.x2)
return(new)
}
# Transform dataframes
coord_cal <-
bern_data |>
dplyr::filter(dataset == "calibration") |>
dplyr::select(site_id_unique, x, y) |>
dplyr::rename(id = site_id_unique, lon = x, lat = y) |>
change_coords(
from_CRS = "+init=epsg:2056",
to_CRS = "+init=epsg:4326"
)
coord_val <-
bern_data |>
dplyr::filter(dataset == "validation") |>
dplyr::select(site_id_unique, x, y) |>
dplyr::rename(id = site_id_unique, lon = x, lat = y) |>
change_coords(
from_CRS = "+init=epsg:2056",
to_CRS = "+init=epsg:4326"
)# Loading packages to improve code readbility to avoid :: notation
# Note: This code may only work when installing the development branch of {leaflet}:
# remotes::install_github('rstudio/leaflet')
library(leaflet)
library(terra)
# Let's get a nice color palette now for easy reference
pal <- colorNumeric(
"magma",
values(r),
na.color = "transparent"
)
# Next, we build a leaflet map
leaflet() |>
# As base maps, use two provided by ESRI
addProviderTiles(providers$Esri.WorldImagery, group = "World Imagery") |>
addProviderTiles(providers$Esri.WorldTopoMap, group = "World Topo") |>
# Add our raster file
addRasterImage(
r,
colors = pal,
opacity = 0.6,
group = "raster"
) |>
# Add markers for sampling locations
leaflet::addCircleMarkers(
data = coord_cal,
lng = ~lon, # Column name for x coordinates
lat = ~lat, # Column name for y coordinates
group = "training",
color = "black"
) |>
leaflet::addCircleMarkers(
data = coord_val,
lng = ~lon, # Column name for x coordinates
lat = ~lat, # Column name for y coordinates
group = "validation",
color = "red"
) |>
# Add some layout and legend
addLayersControl(
baseGroups = c("World Imagery","World Topo"),
position = "topleft",
options = layersControlOptions(collapsed = FALSE),
overlayGroups = c("raster", "training", "validation")
) |>
addLegend(
pal = pal,
values = values(r),
title = "Slope (%)")This plotting example is based to the one shown in the AGDS 2 tutorial “Handful of Pixels” on phenology and more information on using spatial data in R can be found in the Chapter on Geospatial data in R.
That looks great! At first glance, it is a bit crowded but once you scroll in you can investigate our study area quite nicely. You can check whether the slope raster file makes sense by comparing it against the base maps. Can you see how cliffs along the Aare river, hills, and even gravel quarries show high slopes? We also see that our validation dataset is nicely distributed across the area covered by the training dataset.
Now that we have played with a few visualizations, let’s get back to preparing our data. The {terra} package comes with the very useful tool to stack multiple raster on top of each other, if they are of the same spatial format. To do so, we just have to feed in the vector of file names list_raster:
# Load all files as one batch
all_rasters <- terra::rast(list_raster)
all_rastersclass : SpatRaster
dimensions : 986, 2428, 91 (nrow, ncol, nlyr)
resolution : 20, 20 (x, y)
extent : 2568140, 2616700, 1200740, 1220460 (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95
sources : be_gwn25_hdist.tif
be_gwn25_vdist.tif
cindx10_25.tif
... and 88 more source(s)
names : be_gw~hdist, be_gw~vdist, cindx10_25, cindx50_25, geo500h1id, geo500h3id, ...
min values : 0.4998779, 0.000, -100, -99.99999, 1, 0, ...
max values : 3181.2531475, 1165.709, 100, 57.94390, 99, 56, ...
Now, we do not want to have the covariates’ data from all cells in the raster file. Rather, we want to reduce our stacked rasters to the x and y coordinates for which we have soil sampling data. We can do this using the terra::extract() function. Then, we want to merge the two dataframes of soil data and covariates data by their coordinates. Since the order of the covariate data is the same as the Bern data, we can simply bind their columns with cbind():
# Extract coordinates from sampling locations
sampling_xy <- bern_data |> dplyr::select(x, y)
# From all rasters, extract values for sampling coordinates
covar_data <-
terra::extract(all_rasters, # The raster we want to extract from
sampling_xy, # A matrix of x and y values to extract for
ID = FALSE # To not add a default ID column to the output
)
merged_data <- cbind(bern_data, covar_data)
head(merged_data) |> knitr::kable() | site_id_unique | timeset | x | y | dataset | dclass | waterlog.30 | waterlog.50 | waterlog.100 | ph.0.10 | ph.10.30 | ph.30.50 | ph.50.100 | be_gwn25_hdist | be_gwn25_vdist | cindx10_25 | cindx50_25 | geo500h1id | geo500h3id | lgm | lsf | mrrtf25 | mrvbf25 | mt_gh_y | mt_rr_y | mt_td_y | mt_tt_y | mt_ttvar | NegO | PosO | protindx | Se_alti2m_std_50c | Se_conv2m | Se_curv25m | Se_curv2m_fmean_50c | Se_curv2m_fmean_5c | Se_curv2m_s60 | Se_curv2m_std_50c | Se_curv2m_std_5c | Se_curv2m | Se_curv50m | Se_curv6m | Se_curvplan25m | Se_curvplan2m_fmean_50c | Se_curvplan2m_fmean_5c | Se_curvplan2m_s60 | Se_curvplan2m_s7 | Se_curvplan2m_std_50c | Se_curvplan2m_std_5c | Se_curvplan2m | Se_curvplan50m | Se_curvprof25m | Se_curvprof2m_fmean_50c | Se_curvprof2m_fmean_5c | Se_curvprof2m_s60 | Se_curvprof2m_s7 | Se_curvprof2m_std_50c | Se_curvprof2m_std_5c | Se_curvprof2m | Se_curvprof50m | Se_diss2m_50c | Se_diss2m_5c | Se_e_aspect25m | Se_e_aspect2m_5c | Se_e_aspect2m | Se_e_aspect50m | Se_MRRTF2m | Se_MRVBF2m | Se_n_aspect2m_50c | Se_n_aspect2m_5c | Se_n_aspect2m | Se_n_aspect50m | Se_n_aspect6m | Se_NO2m_r500 | Se_PO2m_r500 | Se_rough2m_10c | Se_rough2m_5c | Se_rough2m_rect3c | Se_SAR2m | Se_SCA2m | Se_slope2m_fmean_50c | Se_slope2m_fmean_5c | Se_slope2m_s60 | Se_slope2m_s7 | Se_slope2m_std_50c | Se_slope2m_std_5c | Se_slope2m | Se_slope50m | Se_slope6m | Se_toposcale2m_r3_r50_i10s | Se_tpi_2m_50c | Se_tpi_2m_5c | Se_tri2m_altern_3c | Se_tsc10_2m | Se_TWI2m_s15 | Se_TWI2m_s60 | Se_TWI2m | Se_vrm2m_r10c | Se_vrm2m | terrTextur | tsc25_18 | tsc25_40 | vdcn25 | vszone |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4_26-In-005 | d1968_1974_ptf | 2571994 | 1203001 | validation | poor | 0 | 0 | 1 | 6.071733 | 6.227780 | 7.109235 | 7.214589 | 234.39087 | 1.2986320 | -10.62191 | -6.9658718 | 6 | 0 | 7 | 0.0770846 | 0.0184651 | 4.977099 | 1316.922 | 9931.120 | 58 | 98 | 183 | 1.569110 | 1.534734 | 0.0159717 | 0.3480562 | -40.5395088 | -0.0014441 | -0.0062570 | 0.0175912 | 0.0002296 | 2.9204133 | 1.1769447 | -1.9364884 | 0.0031319 | -0.5886537 | -0.0042508 | -0.0445323 | -0.0481024 | -0.0504083 | -0.1655090 | 1.5687343 | 0.6229440 | -1.0857303 | 0.0007920 | -0.0028067 | -0.0382753 | -0.0656936 | -0.0506380 | -0.0732220 | 1.6507173 | 0.7082230 | 0.8507581 | -0.0023399 | 0.3934371 | 0.1770810 | -0.9702092 | -0.7929600 | -0.5661940 | -0.9939429 | 5.930607 | 6.950892 | -0.2840056 | -0.6084610 | -0.2402939 | -0.0577110 | -0.7661251 | 1.562085 | 1.548762 | 0.3228087 | 0.2241062 | 0.2003846 | 4.000910 | 16.248077 | 0.9428899 | 0.6683306 | 0.9333237 | 0.7310556 | 0.8815832 | 0.3113754 | 1.1250136 | 0.3783818 | 0.5250366 | 0 | -0.0940372 | -0.0583917 | 10.319408 | 0.4645128 | 0.0032796 | 0.0049392 | 0.0011592 | 0.000125 | 0.0002450 | 0.6248673 | 0.3332805 | 1.784737 | 65.62196 | 6 |
| 4_26-In-006 | d1974_1978 | 2572149 | 1202965 | calibration | poor | 0 | 1 | 1 | 6.900000 | 6.947128 | 7.203502 | 7.700000 | 127.41681 | 1.7064546 | -10.87862 | -11.8201790 | 6 | 0 | 7 | 0.0860347 | 0.0544361 | 4.975796 | 1317.000 | 9931.672 | 58 | 98 | 183 | 1.568917 | 1.533827 | 0.0204794 | 0.1484705 | 19.0945148 | -0.0190294 | 0.0021045 | 0.0221433 | 0.0000390 | 3.8783867 | 4.3162045 | 2.1377332 | -0.0171786 | 0.1278165 | -0.0119618 | -0.0501855 | -0.3270764 | -0.1004921 | -0.5133076 | 2.0736780 | 2.2502327 | -0.3522736 | -0.0073879 | 0.0070676 | -0.0522900 | -0.3492197 | -0.1005311 | -0.4981292 | 2.1899190 | 2.4300070 | -2.4900069 | 0.0097907 | 0.4014700 | 0.7360508 | 0.5683194 | 0.8753148 | -0.3505180 | 0.3406741 | 5.984921 | 6.984581 | -0.5732749 | 0.4801802 | 0.4917848 | -0.4550385 | 0.7722272 | 1.543384 | 1.558683 | 0.2730940 | 0.2489859 | 0.2376962 | 4.001326 | 3.357315 | 1.0895698 | 0.9857153 | 1.0231543 | 1.0398037 | 1.0152543 | 0.5357812 | 1.3587183 | 0.0645478 | 0.5793087 | 0 | -0.0014692 | 0.0180000 | 12.603136 | 0.5536283 | 0.0070509 | 0.0067992 | 0.0139006 | 0.000300 | 0.0005389 | 0.7573612 | 0.3395441 | 1.832904 | 69.16074 | 6 |
| 4_26-In-012 | d1974_1978 | 2572937 | 1203693 | calibration | moderate | 0 | 1 | 1 | 6.200000 | 6.147128 | 5.603502 | 5.904355 | 143.41533 | 0.9372618 | 22.10210 | 0.2093917 | 6 | 0 | 7 | 0.0737963 | 3.6830916 | 4.986864 | 1315.134 | 9935.438 | 58 | 98 | 183 | 1.569093 | 1.543057 | 0.0048880 | 0.1112066 | -9.1396294 | 0.0039732 | 0.0009509 | 0.0431735 | 0.0034232 | 0.7022317 | 0.4170935 | -0.4178924 | -0.0026431 | -0.0183221 | 0.0015183 | -0.0079620 | 0.0053904 | -0.0091239 | -0.0110896 | 0.3974485 | 0.2292406 | -0.2168447 | -0.0013561 | -0.0024548 | -0.0089129 | -0.0377831 | -0.0125471 | -0.0052359 | 0.4158890 | 0.2700820 | 0.2010477 | 0.0012870 | 0.6717541 | 0.4404107 | -0.6987815 | -0.3866692 | -0.1960597 | -0.7592779 | 5.953919 | 6.990917 | -0.3006475 | -0.9221049 | -0.9633239 | -0.3257418 | -0.9502072 | 1.565405 | 1.563151 | 0.2305476 | 0.2182523 | 0.1434273 | 4.000320 | 11.330072 | 0.5758902 | 0.5300468 | 0.5107915 | 0.5744110 | 0.4975456 | 0.2001768 | 0.7160403 | 0.1311051 | 0.4620202 | 0 | 0.0340407 | -0.0145804 | 7.100000 | 0.4850160 | 0.0021498 | 0.0017847 | 0.0011398 | 0.000000 | 0.0000124 | 0.7978453 | 0.4455501 | 1.981526 | 63.57096 | 6 |
| 4_26-In-014 | d1974_1978 | 2573374 | 1203710 | validation | well | 0 | 0 | 0 | 6.600000 | 6.754607 | 7.200000 | 7.151129 | 165.80418 | 0.7653937 | -20.11569 | -7.7729993 | 6 | 0 | 7 | 0.0859686 | 0.0075817 | 5.285522 | 1315.160 | 9939.923 | 58 | 98 | 183 | 1.569213 | 1.542792 | 0.0064054 | 0.3710849 | -0.9318936 | -0.0371234 | 0.0029348 | -0.1056513 | 0.0127788 | 1.5150748 | 0.2413423 | -0.0289909 | 0.0020990 | -0.0706228 | -0.0113604 | -0.0301961 | -0.0346193 | -0.0273140 | -0.0343277 | 0.8245047 | 0.1029889 | -0.0272214 | -0.0041158 | 0.0257630 | -0.0331309 | 0.0710320 | -0.0400928 | 0.0529446 | 0.8635767 | 0.1616543 | 0.0017695 | -0.0062147 | 0.4988544 | 0.4217250 | -0.8485889 | -0.8657616 | -0.8836724 | -0.8993938 | 4.856076 | 6.964162 | -0.5735765 | -0.4998477 | -0.4677161 | -0.4121092 | -0.4782534 | 1.562499 | 1.562670 | 0.3859352 | 0.2732429 | 0.1554769 | 4.000438 | 42.167496 | 0.8873205 | 0.8635756 | 0.9015982 | 0.8518201 | 0.5767300 | 0.2149791 | 0.8482135 | 0.3928713 | 0.8432562 | 0 | 0.0686932 | -0.0085602 | 8.303085 | 0.3951114 | 0.0008454 | 0.0021042 | 0.0000000 | 0.000100 | 0.0000857 | 0.4829135 | 0.4483251 | 2.113142 | 64.60535 | 6 |
| 4_26-In-015 | d1968_1974_ptf | 2573553 | 1203935 | validation | moderate | 0 | 0 | 1 | 6.272715 | 6.272715 | 6.718392 | 7.269008 | 61.39244 | 1.0676192 | -55.12566 | -14.0670462 | 6 | 0 | 7 | 0.0650000 | 0.0007469 | 5.894688 | 1315.056 | 9942.032 | 58 | 98 | 183 | 1.570359 | 1.541979 | 0.0042235 | 0.3907509 | 4.2692256 | 0.0378648 | 0.0022611 | -0.1020419 | 0.0161510 | 3.6032522 | 1.8169731 | 0.6409346 | 0.0346340 | 0.0476020 | 0.0378154 | -0.0179657 | -0.0137853 | -0.0146946 | 0.0060875 | 1.4667766 | 0.9816071 | 0.2968794 | 0.0337645 | -0.0000494 | -0.0202268 | 0.0882566 | -0.0308456 | 0.0929077 | 2.6904552 | 1.0218329 | -0.3440553 | -0.0008695 | 0.6999696 | 0.3944107 | -0.8918364 | -0.8864348 | -0.7795515 | -0.4249992 | 4.130917 | 6.945287 | 0.4304937 | 0.4614536 | 0.5919228 | 0.6559467 | 0.4574654 | 1.550528 | 1.562685 | 0.4330348 | 0.3299487 | 0.1889674 | 4.000948 | 5.479310 | 1.8937486 | 1.2098556 | 1.5986075 | 1.2745584 | 2.7759163 | 0.5375320 | 1.2301254 | 0.3582314 | 1.1426100 | 0 | 0.3005829 | 0.0061576 | 10.110727 | 0.5134069 | 0.0043268 | 0.0045225 | 0.0054557 | 0.000200 | 0.0002062 | 0.6290755 | 0.3974232 | 2.080674 | 61.16533 | 6 |
| 4_26-In-016 | d1968_1974_ptf | 2573310 | 1204328 | calibration | poor | 0 | 0 | 1 | 6.272715 | 6.160700 | 5.559031 | 5.161655 | 310.05014 | 0.1321367 | -17.16055 | -28.0693741 | 6 | 0 | 7 | 0.0731646 | 0.0128017 | 5.938320 | 1315.000 | 9940.597 | 58 | 98 | 183 | 1.569434 | 1.541606 | 0.0040683 | 0.1931891 | -0.1732794 | -0.1602274 | -0.0035833 | -0.1282881 | 0.0003549 | 1.5897882 | 0.8171870 | 0.0318570 | -0.0123340 | 0.0400775 | -0.0813964 | -0.0049875 | 0.0320331 | -0.0049053 | 0.0374298 | 0.7912259 | 0.3455668 | 0.0100844 | -0.0059622 | 0.0788309 | -0.0014042 | 0.1603212 | -0.0052602 | 0.0867119 | 1.0207798 | 0.6147888 | -0.0217726 | 0.0063718 | 0.3157751 | 0.5292308 | -0.8766075 | 0.5905659 | 0.8129975 | 0.1640853 | 2.030315 | 6.990967 | 0.6325440 | 0.8054439 | 0.5820994 | 0.7448481 | 0.6081498 | 1.563066 | 1.552568 | 0.3688371 | 0.2607146 | 0.1763995 | 4.000725 | 13.499996 | 1.0418727 | 0.8515157 | 1.2106605 | 0.8916541 | 1.2163279 | 0.4894866 | 1.0906221 | 0.2049688 | 0.7156029 | 0 | -0.0910767 | 0.0034276 | 9.574804 | 0.3864355 | 0.0001476 | 0.0003817 | 0.0000000 | 0.000525 | 0.0001151 | 0.6997021 | 0.4278295 | 2.041467 | 55.78354 | 6 |
Great that worked without problems. Now, to allow spatial trend in more directions than only in a north-south and east-west direction, we have to add polar coordinates:
final_data <-
merged_data |>
dplyr::mutate(
x30 = x*cos(30/180*pi) - y*sin(30/180*pi),
y30 = x*sin(30/180*pi) + y*cos(30/180*pi),
x60 = x*cos(60/180*pi) - y*sin(60/180*pi),
y60 = x*sin(60/180*pi) + y*cos(60/180*pi)
)Not all our covariates may be continuous variables and therefore have should be encoded as factors. As an easy check, we can take the original corvariates data and check for the number of unique values in each raster. If the variable is continuous, we expect that there are a lot of different values - at maximum 1052 different values because we have that many entries. So, let’s have a look and assume that variables with 10 or less different values are categorical variables.
cat_vars <-
covar_data |>
# Get number of distinct values per variable
dplyr::summarise(dplyr::across(dplyr::everything(), ~ dplyr::n_distinct(.))) |>
# Turn df into long format for easy filtering
tidyr::pivot_longer(dplyr::everything(),
names_to = "variable",
values_to = "n") |>
# Filter out variables with 10 or less distinct values
dplyr::filter(n <= 10) |>
# Extract the names of these variables
dplyr::pull('variable')
cat("Variables with less than 10 distinct values:", cat_vars)Variables with less than 10 distinct values: geo500h1id geo500h3id lgm vszone
Now that we have the names of the categorical values, we can mutate these columns in our df using the base function as.factor():
final_data <-
final_data |>
dplyr::mutate(dplyr::across(cat_vars, ~ as.factor(.)))We are almost done with our data preparation, we just need to reduce it to sampling locations for which we have a decent amount of data on the covariates. Else, we blow up the model calibration with data that is not informative enough.
# Get number of rows to calculate percentages
n_rows <- nrow(final_data)
# Get number of distinct values per variable
final_data |>
dplyr::summarise(dplyr::across(dplyr::everything(),
~ length(.) - sum(is.na(.)))) |>
tidyr::pivot_longer(dplyr::everything(),
names_to = "variable",
values_to = "n") |>
dplyr::mutate(perc_available = round(n/n_rows *100)) |>
dplyr::arrange(perc_available) |>
head(10) |>
knitr::kable()| variable | n | perc_available |
|---|---|---|
| ph.30.50 | 856 | 81 |
| ph.10.30 | 866 | 82 |
| ph.50.100 | 859 | 82 |
| timeset | 871 | 83 |
| ph.0.10 | 870 | 83 |
| dclass | 1006 | 96 |
| site_id_unique | 1052 | 100 |
| x | 1052 | 100 |
| y | 1052 | 100 |
| dataset | 1052 | 100 |
This looks good, we have no variable with a substantial amount of missing data. Generally, only pH measurements are lacking, which we should keep in mind when making predictions and inferences. Another great way to explore your data, is using the {visdat} package:
final_data |> visdat::vis_miss()
Alright, we see that we are not missing a lot of data for any variable and in total only 1% of our data is NA. That is good enough! We do not have to modify the dataframe any further and can save it for further analysis.
saveRDS(final_data,
here::here("data/bern_sampling_locations_with_covariates.rds"))